Publications by authors named "Armand R J Girbes"

146 Publications

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

Crit Care Explor 2021 Oct 14;3(10):e0555. Epub 2021 Oct 14.

Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.

Objectives: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients.

Design: Multicenter retrospective cohort study.

Setting: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020.

Patients: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded.

Measurements And Main Results: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao/Fio ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant ( ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves.

Conclusions: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.
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http://dx.doi.org/10.1097/CCE.0000000000000555DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522869PMC
October 2021

Targeted Temperature Management in Out-of-Hospital Cardiac Arrest With Shockable Rhythm: A Post Hoc Analysis of the Coronary Angiography After Cardiac Arrest Trial.

Crit Care Med 2021 Sep 22. Epub 2021 Sep 22.

Department of Cardiology, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands. Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands. Department of Intensive Care Medicine, Erasmus Medical Center, Rotterdam, The Netherlands. Department of Cardiology, Amphia Hospital, Breda, The Netherlands. Department of Intensive Care Medicine, Amphia Hospital, Breda, The Netherlands. Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands. Department of Intensive Care Medicine, Rijnstate Hospital, Arnhem, The Netherlands. Department of Cardiology, HAGA Hospital, Den Haag, The Netherlands. Department of Intensive Care Medicine, HAGA Hospital, Den Haag, The Netherlands. Department of Cardiology, Maasstad Hospital, Rotterdam, The Netherlands. Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands. Department of Intensive Care Medicine, Maasstad Hospital, Rotterdam, The Netherlands. Department of Intensive Care Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands. Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands. Department of Intensive Care Medicine, Maastricht University Medical Center, University Maastricht, Maastricht, The Netherlands. Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. Department of Intensive Care Medicine, Medisch Spectrum Twente, Enschede, The Netherlands. Department of Cardiology, Medisch Spectrum Twente, Enschede, The Netherlands. Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands. Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. Department of Cardiology, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands. Department of Intensive Care Medicine, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands. Department of Cardiology, OLVG, Amsterdam, The Netherlands. Department of Intensive Care Medicine, OLVG, Amsterdam, The Netherlands. Department of Cardiology, Noord West Ziekenhuisgroep, Alkmaar, The Netherlands. Department of Intensive Care Medicine, Noord West Ziekenhuisgroep, Alkmaar, The Netherlands. Department of Cardiology, Maastricht University Medical Center, Maastricht, The Netherlands. Department of Cardiology, Scheper Hospital, Emmen, The Netherlands. Department of Cardiology, Haaglanden Medical Center, Den Haag, The Netherlands. Department of Cardiology, Isala Hospital, Zwolle, The Netherlands. Department of Cardiology, Tergooi Hospital, Blaricum, The Netherlands. Department of Cardiology, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands. Department of Epidemiology and Data Science, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands.

Objectives: The optimal targeted temperature in patients with shockable rhythm is unclear, and current guidelines recommend targeted temperature management with a correspondingly wide range between 32°C and 36°C. Our aim was to study survival and neurologic outcome associated with targeted temperature management strategy in postarrest patients with initial shockable rhythm.

Design: Observational substudy of the Coronary Angiography after Cardiac Arrest without ST-segment Elevation trial.

Setting: Nineteen hospitals in The Netherlands.

Patients: The Coronary Angiography after Cardiac Arrest trial randomized successfully resuscitated patients with shockable rhythm and absence of ST-segment elevation to a strategy of immediate or delayed coronary angiography. In this substudy, 459 patients treated with mild therapeutic hypothermia (32.0-34.0°C) or targeted normothermia (36.0-37.0°C) were included. Allocation to targeted temperature management strategy was at the discretion of the physician.

Interventions: None.

Measurements And Main Results: After 90 days, 171 patients (63.6%) in the mild therapeutic hypothermia group and 129 (67.9%) in the targeted normothermia group were alive (hazard ratio, 0.86 [95% CI, 0.62-1.18]; log-rank p = 0.35; adjusted odds ratio, 0.89; 95% CI, 0.45-1.72). Patients in the mild therapeutic hypothermia group had longer ICU stay (4 d [3-7 d] vs 3 d [2-5 d]; ratio of geometric means, 1.32; 95% CI, 1.15-1.51), lower blood pressures, higher lactate levels, and increased need for inotropic support. Cerebral Performance Category scores at ICU discharge and 90-day follow-up and patient-reported Mental and Physical Health Scores at 1 year were similar in the two groups.

Conclusions: In the context of out-of-hospital cardiac arrest with shockable rhythm and no ST-elevation, treatment with mild therapeutic hypothermia was not associated with improved 90-day survival compared with targeted normothermia. Neurologic outcomes at 90 days as well as patient-reported Mental and Physical Health Scores at 1 year did not differ between the groups.
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http://dx.doi.org/10.1097/CCM.0000000000005271DOI Listing
September 2021

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists.

Crit Care Explor 2021 Sep 10;3(9):e0529. Epub 2021 Sep 10.

Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.

Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning-based real-time bedside decision support tool.

Derivation Cohort: Data from patients admitted to a mixed surgical-medical academic medical center ICU from 2004 to 2016.

Validation Cohort: Data from 2016 to 2019 from the same center.

Prediction Model: Patient characteristics, clinical observations, physiologic measurements, laboratory studies, and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations, and readmission probability-time curves were constructed to identify subgroups. Explainability was established by presenting individualized risk trends and feature importance.

Results: Our final derivation dataset included 14,105 admissions. The combined readmission/mortality rate within 7 days of ICU discharge was 5.3%. Using Gradient Boosting, the model achieved an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.75-0.81) and an area under the precision-recall curve of 0.19 on the validation cohort ( = 3,929). The most predictive features included common physiologic parameters but also less apparent variables like nutritional support. At a 6% risk threshold, the model showed a sensitivity (recall) of 0.72, specificity of 0.70, and a positive predictive value (precision) of 0.15. Impact analysis using probability-time curves and the 6% risk threshold identified specific patient groups at risk and the potential of a change in discharge management to reduce relative risk by 14%.

Conclusions: We developed an explainable machine learning model that may aid in identifying patients at high risk for readmission and mortality after ICU discharge using the first freely available European critical care database, AmsterdamUMCdb. Impact analysis showed that a relative risk reduction of 14% could be achievable, which might have significant impact on patients and society. ICU data sharing facilitates collaboration between intensivists and data scientists to accelerate model development.
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http://dx.doi.org/10.1097/CCE.0000000000000529DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437217PMC
September 2021

Extended Lung Ultrasound to Differentiate Between Pneumonia and Atelectasis in Critically Ill Patients: A Diagnostic Accuracy Study.

Crit Care Med 2021 Sep 27. Epub 2021 Sep 27.

Department of Intensive Care Medicine, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands. Amsterdam Leiden Intensive care Focused Echography (ALIFE, www.alifeofpocus.com), Amsterdam, The Netherlands. Amsterdam Cardiovascular Sciences Research Institute, Amsterdam UMC, Amsterdam, The Netherlands.

Objectives: To determine the diagnostic accuracy of extended lung ultrasonographic assessment, including evaluation of dynamic air bronchograms and color Doppler imaging to differentiate pneumonia and atelectasis in patients with consolidation on chest radiograph. Compare this approach to the Simplified Clinical Pulmonary Infection Score, Lung Ultrasound Clinical Pulmonary Infection Score, and the Bedside Lung Ultrasound in Emergency protocol.

Design: Prospective diagnostic accuracy study.

Setting: Adult ICU applying selective digestive decontamination.

Patients: Adult patients that underwent a chest radiograph for any indication at any time during admission. Patients with acute respiratory distress syndrome, coronavirus disease 2019, severe thoracic trauma, and infectious isolation measures were excluded.

Interventions: None.

Measurements And Main Results: Lung ultrasound was performed within 24 hours of chest radiograph. Consolidated tissue was assessed for presence of dynamic air bronchograms and with color Doppler imaging for presence of flow. Clinical data were recorded after ultrasonographic assessment. The primary outcome was diagnostic accuracy of dynamic air bronchogram and color Doppler imaging alone and within a decision tree to differentiate pneumonia from atelectasis. Of 120 patients included, 51 (42.5%) were diagnosed with pneumonia. The dynamic air bronchogram had a 45% (95% CI, 31-60%) sensitivity and 99% (95% CI, 92-100%) specificity. Color Doppler imaging had a 90% (95% CI, 79-97%) sensitivity and 68% (95% CI, 56-79%) specificity. The combined decision tree had an 86% (95% CI, 74-94%) sensitivity and an 86% (95% CI, 75-93%) specificity. The Bedside Lung Ultrasound in Emergency protocol had a 100% (95% CI, 93-100%) sensitivity and 0% (95% CI, 0-5%) specificity, while the Simplified Clinical Pulmonary Infection Score and Lung Ultrasound Clinical Pulmonary Infection Score had a 41% (95% CI, 28-56%) sensitivity, 84% (95% CI, 73-92%) specificity and 68% (95% CI, 54-81%) sensitivity, 81% (95% CI, 70-90%) specificity, respectively.

Conclusions: In critically ill patients with pulmonary consolidation on chest radiograph, an extended lung ultrasound protocol is an accurate and directly bedside available tool to differentiate pneumonia from atelectasis. It outperforms standard lung ultrasound and clinical scores.
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http://dx.doi.org/10.1097/CCM.0000000000005303DOI Listing
September 2021

Lung ultrasound in a tertiary intensive care unit population: a diagnostic accuracy study.

Crit Care 2021 09 17;25(1):339. Epub 2021 Sep 17.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Location VU University Medical Center, de Boelelaan 11171007MB, Postbox 7505, Amsterdam, The Netherlands.

Background: Evidence from previous studies comparing lung ultrasound to thoracic computed tomography (CT) in intensive care unit (ICU) patients is limited due to multiple methodologic weaknesses. While addressing methodologic weaknesses of previous studies, the primary aim of this study is to investigate the diagnostic accuracy of lung ultrasound in a tertiary ICU population.

Methods: This is a single-center, prospective diagnostic accuracy study conducted at a tertiary ICU in the Netherlands. Critically ill patients undergoing thoracic CT for any clinical indication were included. Patients were excluded if time between the index and reference test was over eight hours. Index test and reference test consisted of 6-zone lung ultrasound and thoracic CT, respectively. Hemithoraces were classified by the index and reference test as follows: consolidation, interstitial syndrome, pneumothorax and pleural effusion. Sensitivity, specificity, positive and negative likelihood ratio were estimated.

Results: In total, 87 patients were included of which eight exceeded the time limit and were subsequently excluded. In total, there were 147 respiratory conditions in 79 patients. The estimated sensitivity and specificity to detect consolidation were 0.76 (95%CI: 0.68 to 0.82) and 0.92 (0.87 to 0.96), respectively. For interstitial syndrome they were 0.60 (95%CI: 0.48 to 0.71) and 0.69 (95%CI: 0.58 to 0.79). For pneumothorax they were 0.59 (95%CI: 0.33 to 0.82) and 0.97 (95%CI: 0.93 to 0.99). For pleural effusion they were 0.85 (95%CI: 0.77 to 0.91) and 0.77 (95%CI: 0.62 to 0.88).

Conclusions: In conclusion, lung ultrasound is an adequate diagnostic modality in a tertiary ICU population to detect consolidations, interstitial syndrome, pneumothorax and pleural effusion. Moreover, one should be careful not to interpret lung ultrasound results in deterministic fashion as multiple respiratory conditions can be present in one patient. Trial registration This study was retrospectively registered at Netherlands Trial Register on March 17, 2021, with registration number NL9344.
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http://dx.doi.org/10.1186/s13054-021-03759-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447620PMC
September 2021

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

Crit Care 2021 08 23;25(1):304. Epub 2021 Aug 23.

Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands.

Background: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients.

Methods: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers.

Results: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive.

Conclusions: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.
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http://dx.doi.org/10.1186/s13054-021-03733-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8381710PMC
August 2021

Early high-dose vitamin C in post-cardiac arrest syndrome (VITaCCA): study protocol for a randomized, double-blind, multi-center, placebo-controlled trial.

Trials 2021 Aug 18;22(1):546. Epub 2021 Aug 18.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Cardiovascular Science (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

Background: High-dose intravenous vitamin C directly scavenges and decreases the production of harmful reactive oxygen species (ROS) generated during ischemia/reperfusion after a cardiac arrest. The aim of this study is to investigate whether short-term treatment with a supplementary or very high-dose intravenous vitamin C reduces organ failure in post-cardiac arrest patients.

Methods: This is a double-blind, multi-center, randomized placebo-controlled trial conducted in 7 intensive care units (ICUs) in The Netherlands. A total of 270 patients with cardiac arrest and return of spontaneous circulation will be randomly assigned to three groups of 90 patients (1:1:1 ratio, stratified by site and age). Patients will intravenously receive a placebo, a supplementation dose of 3 g of vitamin C or a pharmacological dose of 10 g of vitamin C per day for 96 h. The primary endpoint is organ failure at 96 h as measured by the Resuscitation-Sequential Organ Failure Assessment (R-SOFA) score at 96 h minus the baseline score (delta R-SOFA). Secondary endpoints are a neurological outcome, mortality, length of ICU and hospital stay, myocardial injury, vasopressor support, lung injury score, ventilator-free days, renal function, ICU-acquired weakness, delirium, oxidative stress parameters, and plasma vitamin C concentrations.

Discussion: Vitamin C supplementation is safe and preclinical studies have shown beneficial effects of high-dose IV vitamin C in cardiac arrest models. This is the first RCT to assess the clinical effect of intravenous vitamin C on organ dysfunction in critically ill patients after cardiac arrest.

Trial Registration: ClinicalTrials.gov NCT03509662. Registered on April 26, 2018. https://clinicaltrials.gov/ct2/show/NCT03509662 European Clinical Trials Database (EudraCT): 2017-004318-25. Registered on June 8, 2018. https://www.clinicaltrialsregister.eu/ctr-search/trial/2017-004318-25/NL.
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http://dx.doi.org/10.1186/s13063-021-05483-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371424PMC
August 2021

Rapid screening of critically ill patients for low plasma vitamin C concentrations using a point-of-care oxidation-reduction potential measurement.

Intensive Care Med Exp 2021 Aug 9;9(1):40. Epub 2021 Aug 9.

Department of Intensive Care Medicine, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

Background: Hypovitaminosis C and vitamin C deficiency are common in critically ill patients and associated with organ dysfunction. Low vitamin C status often goes unnoticed because determination is challenging. The static oxidation reduction potential (sORP) reflects the amount of oxidative stress in the blood and is a potential suitable surrogate marker for vitamin C. sORP can be measured rapidly using the RedoxSYS system, a point-of-care device. This study aims to validate a model that estimates plasma vitamin C concentration and to determine the diagnostic accuracy of sORP to discriminate between decreased and higher plasma vitamin C concentrations.

Methods: Plasma vitamin C concentrations and sORP were measured in a mixed intensive care (IC) population. Our model estimating vitamin C from sORP was validated by assessing its accuracy in two datasets. Receiver operating characteristic (ROC) curves with areas under the curve (AUC) were constructed to show the diagnostic accuracy of sORP to identify and rule out hypovitaminosis C and vitamin C deficiency. Different cut-off values are provided.

Results: Plasma vitamin C concentration and sORP were measured in 117 samples in dataset 1 and 43 samples in dataset 2. Bias and precision (SD) were 1.3 ± 10.0 µmol/L and 3.9 ± 10.1 µmol/L in dataset 1 and 2, respectively. In patients with low plasma vitamin C concentrations, bias and precision were - 2.6 ± 5.1 µmol/L and - 1.1 ± 5.4 µmol in dataset 1 (n = 40) and 2 (n = 20), respectively. Optimal sORP cut-off values to differentiate hypovitaminosis C and vitamin C deficiency from higher plasma concentrations were found at 114.6 mV (AUC 0.91) and 124.7 mV (AUC 0.93), respectively.

Conclusion: sORP accurately estimates low plasma vitamin C concentrations and can be used to screen for hypovitaminosis C and vitamin C deficiency in critically ill patients. A validated model and multiple sORP cut-off values are presented for subgroup analysis in clinical trials or usage in clinical practice.
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http://dx.doi.org/10.1186/s40635-021-00403-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349944PMC
August 2021

Breathing variability-implications for anaesthesiology and intensive care.

Crit Care 2021 08 5;25(1):280. Epub 2021 Aug 5.

Departments of Anesthesiology and Intensive Care, Amsterdam UMC, VUMC, ZH 6F 003, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.
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http://dx.doi.org/10.1186/s13054-021-03716-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339683PMC
August 2021

Endothelium-associated biomarkers mid-regional proadrenomedullin and C-terminal proendothelin-1 have good ability to predict 28-day mortality in critically ill patients with SARS-CoV-2 pneumonia: A prospective cohort study.

J Crit Care 2021 12 20;66:173-180. Epub 2021 Jul 20.

Department of Intensive Care Medicine, Amsterdam UMC, Medical Centres, VU University Medical Centre, Amsterdam, the Netherlands. Electronic address:

Purpose: We assessed the ability of mid-regional proadrenomedullin (MR-proADM) and C-terminal proendothelin-1 (CT-proET-1) to predict 28-day mortality in critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia.

Methods: Biomarkers were collected during the first seven days in this prospective observational cohort study. We investigated the relationship between biomarkers and mortality in a multivariable Cox regression model adjusted for age and SOFA score.

Results: In 105 critically ill patients with confirmed SARS-CoV-2 pneumonia 28-day mortality was 28.6%. MR-proADM and CT-proET-1 were significantly higher in 28-day non-survivors at baseline and over time. ROC curves revealed high accuracy to identify non-survivors for baseline MR-proADM and CT-proET-1, AUC 0.84, (95% CI 0.76-0.92), p < 0.001 and 0.79, (95% CI 0.69-0.89), p < 0.001, respectively. The AUC for prediction of 28-day mortality for MR-proADM and CT-proET-1 remained high over time. MR-proADM ≥1.57 nmol/L and CT-proET-1 ≥ 111 pmol/L at baseline were significant predictors for 28-day mortality (HR 6.80, 95% CI 3.12-14.84, p < 0.001 and HR 3.72, 95% CI 1.71-8.08, p 0.01).

Conclusion: Baseline and serial MR-proADM and CT-proET-1 had good ability to predict 28-day mortality in critically ill patients with SARS-CoV-2 pneumonia.

Trial Registration: NEDERLANDS TRIAL REGISTER, NL8460.
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http://dx.doi.org/10.1016/j.jcrc.2021.07.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289696PMC
December 2021

Incidence, Clinical Characteristics and Outcomes of Early Hyperbilirubinemia in Critically ill Patients - Insights From The Mars Study.

Shock 2021 Jul 7. Epub 2021 Jul 7.

Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands: Department of Anesthesiology Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands: Department of Intensive Care Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands: Research VUmc Intensive Care (REVIVE) Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, The Netherlands: Department of Intensive Care Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, The Netherlands: Department of Pulmonology Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, The Netherlands: Department of Gastroenterology and Hepatology Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, The Netherlands: Division of Infectious Diseases Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, The Netherlands: Center for Experimental and Molecular Medicine (CEMM) OLVG hospital, Amsterdam, The Netherlands: Department of Intensive Care Medicine University Medical Center Utrecht, Utrecht, The Netherlands: Department of Intensive Care Medicine University Medical Center Utrecht, Utrecht, The Netherlands: Department of Medical Microbiology Mahidol University, Bangkok, Thailand: Mahidol-Oxford Tropical Medicine Research Unit (MORU) Mahidol University, Bangkok, Thailand: University of Oxford, Oxford, UK: Nuffield Department of Medicine.

Objective: To investigate the incidence, clinical characteristics and outcomes of early hyperbilirubinemia in critically ill patients.

Design And Setting: This is a post-hoc analysis of a prospective multicenter cohort study.

Patients: Patients with measured bilirubin levels within the first 2 days after ICU admission were eligible. Patients with liver cirrhosis were excluded.

Endpoints: The primary endpoint was the incidence of early hyperbilirubinemia, defined as bilirubin ≥ 33 μmol/L within 2 days after ICU admission. Secondary endpoints included clinical characteristics of patients with versus patients without early hyperbilirubinemia, and outcomes up to day 30.

Results: Of 4836 patients, 559 (11.6%) patients had early hyperbilirubinemia. Compared to patients without early hyperbilirubinemia, patients with early hyperbilirubinemia presented with higher severity of illness scores, and higher incidences of sepsis and organ failure. After adjustment for confounding variables, early hyperbilirubinemia remained associated with mortality at day 30 (odds ratio, 1.31 [95%-confidence interval 1.06-1.60]; p = 0.018). Patients with early hyperbilirubinemia and thrombocytopenia (interaction p-value = 0.005) had a higher likelihood of death within 30 days (odds ratio, 2.61 [95%-confidence interval 2.08-3.27]; p < 0.001) than patients with early hyperbilirubinemia and a normal platelet count (odds ratio, 1.09 [95%-confidence interval 0.75-1.55]; p = 0.655).

Conclusions: Early hyperbilirubinemia occurs frequently in the critically ill, and these patients present with higher disease severity and more often with sepsis and organ failures. Early hyperbilirubinemia has an association with mortality, albeit this association was only found in patients with concomitant thrombocytopenia.
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http://dx.doi.org/10.1097/SHK.0000000000001836DOI Listing
July 2021

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

Intensive Care Med Exp 2021 Jun 28;9(1):32. Epub 2021 Jun 28.

ICU, Maasstad Ziekenhuis Rotterdam, Rotterdam, The Netherlands.

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients.

Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split.

Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmHO.

Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.
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http://dx.doi.org/10.1186/s40635-021-00397-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236316PMC
June 2021

Duration of antibiotic treatment using procalcitonin-guided treatment algorithms in older patients: a patient-level meta-analysis from randomized controlled trials.

Age Ageing 2021 09;50(5):1546-1556

Critical Care and Peri-operative Medicine, Monash Health, Melbourne, Australia.

Background: Older patients have a less pronounced immune response to infection, which may also influence infection biomarkers. There is currently insufficient data regarding clinical effects of procalcitonin (PCT) to guide antibiotic treatment in older patients.

Objective And Design: We performed an individual patient data meta-analysis to investigate the association of age on effects of PCT-guided antibiotic stewardship regarding antibiotic use and outcome.

Subjects And Methods: We had access to 9,421 individual infection patients from 28 randomized controlled trials comparing PCT-guided antibiotic therapy (intervention group) or standard care. We stratified patients according to age in four groups (<75 years [n = 7,079], 75-80 years [n = 1,034], 81-85 years [n = 803] and >85 years [n = 505]). The primary endpoint was the duration of antibiotic treatment and the secondary endpoints were 30-day mortality and length of stay.

Results: Compared to control patients, mean duration of antibiotic therapy in PCT-guided patients was significantly reduced by 24, 22, 26 and 24% in the four age groups corresponding to adjusted differences in antibiotic days of -1.99 (95% confidence interval [CI] -2.36 to -1.62), -1.98 (95% CI -2.94 to -1.02), -2.20 (95% CI -3.15 to -1.25) and - 2.10 (95% CI -3.29 to -0.91) with no differences among age groups. There was no increase in the risk for mortality in any of the age groups. Effects were similar in subgroups by infection type, blood culture result and clinical setting (P interaction >0.05).

Conclusions: This large individual patient data meta-analysis confirms that, similar to younger patients, PCT-guided antibiotic treatment in older patients is associated with significantly reduced antibiotic exposures and no increase in mortality.
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http://dx.doi.org/10.1093/ageing/afab078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437072PMC
September 2021

Determining a target SpO2 to maintain PaO2 within a physiological range.

PLoS One 2021 13;16(5):e0250740. Epub 2021 May 13.

Institute for Cardiovascular Research (ICaR-VU), Amsterdam UMC, Amsterdam, Noord-Holland, The Netherlands.

Objective: In the context of an ongoing debate on the potential risks of hypoxemia and hyperoxemia, it seems prudent to maintain the partial arterial oxygen pressure (PaO2) in a physiological range during administration of supplemental oxygen. The PaO2 and peripheral oxygen saturation (SpO2) are closely related and both are used to monitor oxygenation status. However, SpO2 values cannot be used as an exact substitute for PaO2. The aim of this study in acutely ill and stable patients was to determine at which SpO2 level PaO2 is more or less certain to be in the physiological range.

Methods: This is an observational study prospectively collecting data pairs of PaO2 and SpO2 values in patients admitted to the emergency room or intensive care unit (Prospective Inpatient Acutely ill cohort; PIA cohort). A second cohort of retrospective data of patients who underwent pulmonary function testing was also included (Retrospective Outpatient Pulmonary cohort; ROP cohort). Arterial hypoxemia was defined as PaO2 < 60 mmHg and hyperoxemia as PaO2 > 125 mmHg. The SpO2 cut-off values with the lowest risk of hypoxemia and hyperoxemia were determined as the 95th percentile of the observed SpO2 values corresponding with the observed hypoxemic and hyperoxemic PaO2 values.

Results: 220 data pairs were collected in the PIA cohort. 95% of hypoxemic PaO2 measurements occurred in patients with an SpO2 below 94%, and 95% of hyperoxemic PaO2 measurements occurred in patients with an SpO2 above 96%. Additionally in the 1379 data pairs of the ROP cohort, 95% of hypoxemic PaO2 measurements occurred in patients with an SpO2 below 93%.

Conclusion: The SpO2 level marking an increased risk of arterial hypoxemia is not substantially different in acutely ill versus stable patients. In acutely ill patients receiving supplemental oxygen an SpO2 target of 95% maximizes the likelihood of maintaining PaO2 in the physiological range.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250740PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118260PMC
October 2021

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

Crit Care Med 2021 06;49(6):e563-e577

Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Vrije Universiteit, Universiteit van Amsterdam, Amsterdam, The Netherlands.

Objectives: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data.

Setting: University hospital ICU.

Subjects: Data from ICU patients admitted between 2003 and 2016.

Interventions: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database.

Measurements And Main Results: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous.

Conclusions: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.
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http://dx.doi.org/10.1097/CCM.0000000000004916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132908PMC
June 2021

Coronavirus disease 2019 is associated with catheter-related thrombosis in critically ill patients: A multicenter case-control study.

Thromb Res 2021 04 26;200:87-90. Epub 2021 Jan 26.

Department of Intensive Care Medicine, Amsterdam University Medical Centers, VU University, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences Research Institute, Amsterdam University Medical Centers, Amsterdam, the Netherlands; Amsterdam Leiden Intensive care Focused Echography (ALIFE, www.alifeofpocus.com), the Netherlands.

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http://dx.doi.org/10.1016/j.thromres.2021.01.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835604PMC
April 2021

Alkaline phosphatase in pulmonary inflammation-a translational study in ventilated critically ill patients and rats.

Intensive Care Med Exp 2020 Dec 18;8(Suppl 1):46. Epub 2020 Dec 18.

Department of Intensive Care, Amsterdam University Medical Centers, location "VU", Mail stop ZH 7D-172, De Boelelaan 1117, 1082 RW, Amsterdam, the Netherlands.

Background: Alkaline phosphatase (AP), a dephosphorylating enzyme, is involved in various physiological processes and has been shown to have anti-inflammatory effects.

Aim: To determine the correlation between pulmonary AP activity and markers of inflammation in invasively ventilated critically ill patients with or without acute respiratory distress syndrome (ARDS), and to investigate the effect of administration of recombinant AP on pulmonary inflammation in a well-established lung injury model in rats METHODS: AP activity was determined and compared with levels of various inflammatory mediators in bronchoalveolar lavage fluid (BALF) samples obtained from critically ill patients within 2 days of start of invasive ventilation. The endpoints of this part of the study were the correlations between AP activity and markers of inflammation, i.e., interleukin (IL)-6 levels in BALF. In RccHan Wistar rats, lung injury was induced by intravenous administration of 10 mg/kg lipopolysaccharide, followed by ventilation with a high tidal volume for 4 h. Rats received either an intravenous bolus of 1500 IU/kg recombinant AP or normal saline 2 h after intravenous LPS administration, right before start of ventilation. Endpoints of this part of the study were pulmonary levels of markers of inflammation, including IL-6, and markers of endothelial and epithelial dysfunction.

Results: BALF was collected from 83 patients; 10 patients had mild ARDS, and 15 had moderate to severe ARDS. AP activity correlated well with levels of IL-6 (r = 0.70), as well as with levels of other inflammatory mediators. Pulmonary AP activity between patients with and without ARDS was comparable (0.33 [0.14-1.20] vs 0.55 [0.21-1.42] U/L; p = 0.37). Animals with acute lung injury had markedly elevated pulmonary AP activity compared to healthy controls (2.58 [2.18-3.59] vs 1.01 [0.80-1.46] U/L; p < 0.01). Intravenous administration of recombinant AP did neither affect pulmonary inflammation nor endothelial and epithelial dysfunction.

Conclusions: In ventilated critically ill patients, pulmonary AP activity correlates well with markers of pulmonary inflammation, such as IL-6 and IL-8. In animals with lung injury, pulmonary AP activity is elevated. Administration of recombinant AP does not alter pulmonary inflammation and endothelial or epithelial dysfunction in the acute phase of a murine lung injury model.
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http://dx.doi.org/10.1186/s40635-020-00335-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746537PMC
December 2020

Lung ultrasound findings in patients with novel SARS-CoV-2.

ERJ Open Res 2020 Oct 16;6(4). Epub 2020 Nov 16.

Dept of Intensive Care Medicine, Amsterdam University Medical Centers, VUmc, Amsterdam, The Netherlands.

Background: Over 2 million people worldwide have been infected with severe acute respiratory distress syndrome-coronavirus-2 (SARS CoV-2). Lung ultrasound has been proposed to diagnose and monitor it, despite the fact that little is known about the ultrasound appearance due to the novelty of the illness. The aim of this manuscript is to characterise the lung ultrasonographic appearance of critically ill patients with SARS-CoV-2 pneumonia, with particular emphasis on its relationship with the time course of the illness and clinical parameters.

Methods: Adult patients from the intensive care unit of two academic hospitals who tested positive for SARS-CoV-2 were included. Images were analysed using internationally recognised techniques which included assessment of the pleura, number of B-lines, pathology in the PLAPS (posterolateral alveolar and/or pleural syndrome) point, bedside lung ultrasound in emergency profiles, and the lung ultrasound score. The primary outcomes were frequencies, percentages and differences in lung ultrasound findings overall and between short (≤14 days) and long (>14 days) durations of symptoms and their correlation with clinical parameters.

Results: In this pilot observational study, 61 patients were included with 76 examinations available for analysis. 26% of patients had no anterior lung abnormalities, while the most prevalent pathological ultrasound findings were thickening of the pleura (42%), ≥3 B-lines per view (38%) and presence of PLAPS (74%). Patients with "long" duration of symptoms presented more frequently with a thickened and irregular pleura (32 (21%) 11 (9%)), C-profile (18 (47%) 8 (25%)) and pleural effusion (14 (19%) 3 (5%)), compared to patients with short duration of symptoms. Lung ultrasound findings did not correlate with arterial oxygen tension/inspiratory oxygen fraction ratio, fluid balance or dynamic compliance.

Conclusion: SARS-CoV-2 results in significant, but not specific, ultrasound changes, with decreased lung sliding, thickening of the pleura and a B-profile being the most commonly observed. With time, a thickened and irregular pleura, C-profile and pleural effusion become more common findings. When screening patients, a comprehensive ultrasound protocol might be necessary.
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http://dx.doi.org/10.1183/23120541.00238-2020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7548922PMC
October 2020

Breath-synchronized electrical stimulation of the expiratory muscles in mechanically ventilated patients: a randomized controlled feasibility study and pooled analysis.

Crit Care 2020 10 30;24(1):628. Epub 2020 Oct 30.

Department of Intensive Care Medicine, Amsterdam University Medical Centers, location VUmc, Postbox 7505, 1007 MB, Amsterdam, The Netherlands.

Background: Expiratory muscle weakness leads to difficult ventilator weaning. Maintaining their activity with functional electrical stimulation (FES) may improve outcome. We studied feasibility of breath-synchronized expiratory population muscle FES in a mixed ICU population ("Holland study") and pooled data with our previous work ("Australian study") to estimate potential clinical effects in a larger group.

Methods: Holland: Patients with a contractile response to FES received active or sham expiratory muscle FES (30 min, twice daily, 5 days/week until weaned). Main endpoints were feasibility (e.g., patient recruitment, treatment compliance, stimulation intensity) and safety. Pooled: Data on respiratory muscle thickness and ventilation duration from the Holland and Australian studies were combined (N = 40) in order to estimate potential effect size. Plasma cytokines (day 0, 3) were analyzed to study the effects of FES on systemic inflammation.

Results: Holland: A total of 272 sessions were performed (active/sham: 169/103) in 20 patients (N = active/sham: 10/10) with a total treatment compliance rate of 91.1%. No FES-related serious adverse events were reported. Pooled: On day 3, there was a between-group difference (N = active/sham: 7/12) in total abdominal expiratory muscle thickness favoring the active group [treatment difference (95% confidence interval); 2.25 (0.34, 4.16) mm, P = 0.02] but not on day 5. Plasma cytokine levels indicated that early FES did not induce systemic inflammation. Using a survival analysis approach for the total study population, median ventilation duration and ICU length of stay were 10 versus 52 (P = 0.07), and 12 versus 54 (P = 0.03) days for the active versus sham group. Median ventilation duration of patients that were successfully extubated was 8.5 [5.6-12.2] versus 10.5 [5.3-25.6] days (P = 0.60) for the active (N = 16) versus sham (N = 10) group, and median ICU length of stay was 10.5 [8.0-14.5] versus 14.0 [9.0-19.5] days (P = 0.36) for those active (N = 16) versus sham (N = 8) patients that were extubated and discharged alive from the ICU. During ICU stay, 3/20 patients died in the active group versus 8/20 in the sham group (P = 0.16).

Conclusion: Expiratory muscle FES is feasible in selected ICU patients and might be a promising technique within a respiratory muscle-protective ventilation strategy. The next step is to study the effects on weaning and ventilator liberation outcome.

Trial Registration: ClinicalTrials.gov, ID NCT03453944. Registered 05 March 2018-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03453944 .
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http://dx.doi.org/10.1186/s13054-020-03352-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596623PMC
October 2020

Observational Research for Therapies Titrated to Effect and Associated With Severity of Illness: Misleading Results From Commonly Used Statistical Methods.

Crit Care Med 2020 12;48(12):1720-1728

Department of Intensive Care, Amsterdam UMC, location VUmc, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.

Objectives: In critically ill patients, treatment dose or intensity is often related to severity of illness and mortality risk, whereas overtreatment or undertreatment (relative to the individual need) may further increase the odds of death. We aimed to investigate how these relationships affect the results of common statistical methods used in observational studies.

Design: Using Monte Carlo simulation, we generated data for 5,000 patients with a treatment dose related to the pretreatment mortality risk but with randomly distributed overtreatment or undertreatment. Significant overtreatment or undertreatment (relative to the optimal dose) further increased the mortality risk. A prognostic score that reflects the mortality risk and an outcome of death or survival was then generated. The study was analyzed: 1) using logistic regression to estimate the effect of treatment dose on outcome while controlling for prognostic score and 2) using propensity score matching and inverse probability weighting of the effect of high treatment dose on outcome. The data generation and analyses were repeated 1,500 times over sample sizes between 200 and 30,000 patients, with an increasing accuracy of the prognostic score and with different underlying assumptions.

Setting: Computer-simulated studies.

Measurements And Main Results: In the simulated 5,000-patient observational study, higher treatment dose was found to be associated with increased odds of death (p = 0.00001) while controlling for the prognostic score with logistic regression. Propensity-matched analysis led to similar results. Larger sample sizes led to equally biased estimates with narrower CIs. A perfect risk predictor negated the bias only under artificially perfect assumptions.

Conclusions: When a treatment dose is associated with severity of illness and should be dosed "enough," logistic regression, propensity score matching, and inverse probability weighting to adjust for confounding by severity of illness lead to biased results. Larger sample sizes lead to more precisely wrong estimates.
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http://dx.doi.org/10.1097/CCM.0000000000004612DOI Listing
December 2020

Association of kidney function with effectiveness of procalcitonin-guided antibiotic treatment: a patient-level meta-analysis from randomized controlled trials.

Clin Chem Lab Med 2020 09 28;59(2):441-453. Epub 2020 Sep 28.

Medical University Department, Kantonsspital Aarau, Aarau, Switzerland.

Objectives: Patients with impaired kidney function have a significantly slower decrease of procalcitonin (PCT) levels during infection. Our aim was to study PCT-guided antibiotic stewardship and clinical outcomes in patients with impairments of kidney function as assessed by creatinine levels measured upon hospital admission.

Methods: We pooled and analyzed individual data from 15 randomized controlled trials who were randomly assigned to receive antibiotic therapy based on a PCT-algorithms or based on standard of care. We stratified patients on the initial glomerular filtration rate (GFR, ml/min/1.73 m2) in three groups (GFR >90 [chronic kidney disease; CKD 1], GFR 15-89 [CKD 2-4] and GFR<15 [CKD 5]). The main efficacy and safety endpoints were duration of antibiotic treatment and 30-day mortality.

Results: Mean duration of antibiotic treatment was significantly shorter in PCT-guided (n=2,492) compared to control patients (n=2,510) (9.5-7.6 days; adjusted difference in days -2.01 [95% CI, -2.45 to -1.58]). CKD 5 patients had overall longer treatment durations, but a 2.5-day reduction in treatment duration was still found in patients receiving in PCT-guided care (11.3 vs. 8.6 days [95% CI -3.59 to -1.40]). There were 397 deaths in 2,492 PCT-group patients (15.9%) compared to 460 deaths in 2,510 control patients (18.3%) (adjusted odds ratio, 0.88 [95% CI 0.78 to 0.98)]. Effects of PCT-guidance on antibiotic treatment duration and mortality were similar in subgroups stratified by infection type and clinical setting (p interaction >0.05).

Conclusions: This individual patient data meta-analysis confirms that the use of PCT in patients with impaired kidney function, as assessed by admission creatinine levels, is associated with shorter antibiotic courses and lower mortality rates.
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http://dx.doi.org/10.1515/cclm-2020-0931DOI Listing
September 2020

Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.

Pharm Res 2020 Aug 23;37(9):171. Epub 2020 Aug 23.

Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.

Purpose: Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM.

Methods: We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days.

Results: The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were - 7.7%, -4.5% and - 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors.

Conclusions: The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.
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http://dx.doi.org/10.1007/s11095-020-02908-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443423PMC
August 2020

The effect of small versus large clog size on emergency response time: A randomized controlled trial.

J Crit Care 2020 12 8;60:116-119. Epub 2020 Aug 8.

Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.

Objectives: To assess the effect on healthcare professional emergency response time and safety of small compared to large clog size.

Design: Randomized controlled trial.

Setting: The intensive care unit of a single university medical centre in The Netherlands.

Participants: Intensive care medicine professionals.

Interventions: Participants were randomized to wear European size 38 clogs (US male size 6½, US female size 7½) or European size 47 clogs (US male size 13½, US female size 14½) clogs and were required to run a 125 m course from the coffee break room to the elevator providing access to the emergency department.

Main Outcome Measures: The primary outcome was the time to complete the running course. Height, shoe size, self-described fitness, age and staff category were investigated as possible effect modifiers. Secondary endpoints were reported clog comfort and suspected unexpected clog-related adverse events (SUCRAEs).

Results: 50 participants were randomized (25 to European size 38 clogs and 25 to size 47 clogs). Mean age was 37 years (SD 12) and 29 participants (58%) were female. The primary outcome was 4.4 s (95% CI -7.1; -1.6) faster in the size 5 clogs group compared to the size 12 clogs group. This effect was not modified by any of the predefined participant characteristics. No differences were found in reported clog comfort or SUCRAEs.

Conclusions: European size 38 clogs lead to faster emergency response times than size 47 clogs.

Trial Registration: NCT04406220.
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http://dx.doi.org/10.1016/j.jcrc.2020.07.028DOI Listing
December 2020

Why we should sample sparsely and aim for a higher target: Lessons from model-based therapeutic drug monitoring of vancomycin in intensive care patients.

Br J Clin Pharmacol 2021 03 17;87(3):1234-1242. Epub 2020 Aug 17.

Department of Intensive Care Medicine, Amsterdam Cardiovascular Sciences, Amsterdam Medical Data Science, Research VUmc Intensive Care, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Aims: To explore the optimal data sampling scheme and the pharmacokinetic (PK) target exposure on which dose computation is based in the model-based therapeutic drug monitoring (TDM) practice of vancomycin in intensive care (ICU) patients.

Methods: We simulated concentration data for 1 day following four sampling schemes, C , C + C , C + C + C , and rich sampling where a sample was drawn every hour within a dose interval. The datasets were used for Bayesian estimation to obtain PK parameters, which were used to compute the doses for the next day based on five PK target exposures: AUC = 400, 500, and 600 mg·h/L and C = 15 and 20 mg/L. We then simulated data for the next day, adopting the computed doses, and repeated the above procedure for 7 days. Thereafter, we calculated the percentage error and the normalized root mean square error (NRMSE) of estimated against "true" PK parameters, and the percentage of optimal treatment (POT), defined as the percentage of patients who met 400 ≤ AUC ≤ 600 mg·h/L and C ≤ 20 mg/L.

Results: PK parameters were unbiasedly estimated in all investigated scenarios and the 6-day average NRMSE were 32.5%/38.5% (CL/V, where CL is clearance and V is volume of distribution) in the trough sampling scheme and 27.3%/26.5% (CL/V) in the rich sampling scheme. Regarding POT, the sampling scheme had marginal influence, while target exposure showed clear impacts that the maximum POT of 71.5% was reached when doses were computed based on AUC = 500 mg·h/L.

Conclusions: For model-based TDM of vancomycin in ICU patients, sampling more frequently than taking only trough samples adds no value and dosing based on AUC = 500 mg·h/L lead to the best POT.
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http://dx.doi.org/10.1111/bcp.14498DOI Listing
March 2021

Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients.

Front Pharmacol 2020 15;11:646. Epub 2020 May 15.

Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Research VUmc Intensive Care (REVIVE), Amsterdam Cardiovascular Science (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

Introduction: Antibiotic dosing in critically ill patients is challenging because their pharmacokinetics (PK) are altered and may change rapidly with disease progression. Standard dosing frequently leads to inadequate PK exposure. Therapeutic drug monitoring (TDM) offers a potential solution but requires sampling and PK knowledge, which delays decision support. It is our philosophy that antibiotic dosing support should be directly available at the bedside through deep integration into the electronic health record (EHR) system. Therefore we developed AutoKinetics, a clinical decision support system (CDSS) for real time, model informed precision antibiotic dosing.

Objective: To provide a detailed description of the design, development, validation, testing, and implementation of AutoKinetics.

Methods: We created a development framework and used workflow analysis to facilitate integration into popular EHR systems. We used a development cycle to iteratively adjust and expand AutoKinetics functionalities. Furthermore, we performed a literature review to select and integrate pharmacokinetic models for five frequently prescribed antibiotics for sepsis. Finally, we tackled regulatory challenges, in particular those related to the Medical Device Regulation under the European regulatory framework.

Results: We developed a SQL-based relational database as the backend of AutoKinetics. We developed a data loader to retrieve data in real time. We designed a clinical dosing algorithm to find a dose regimen to maintain antibiotic pharmacokinetic exposure within clinically relevant safety constraints. If needed, a loading dose is calculated to minimize the time until steady state is achieved. Finally, adaptive dosing using Bayesian estimation is applied if plasma levels are available. We implemented support for five extensively used antibiotics following model development, calibration, and validation. We integrated AutoKinetics into two popular EHRs (Metavision, Epic) and developed a user interface that provides textual and visual feedback to the physician.

Conclusion: We successfully developed a CDSS for real time model informed precision antibiotic dosing at the bedside of the critically ill. This holds great promise for improving sepsis outcome. Therefore, we recently started the Right Dose Right Now multi-center randomized control trial to validate this concept in 420 patients with severe sepsis and septic shock.
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http://dx.doi.org/10.3389/fphar.2020.00646DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7243359PMC
May 2020

The attributable mortality of acute respiratory distress syndrome.

Intensive Care Med 2020 07 27;46(7):1508-1509. Epub 2020 Apr 27.

Department of Intensive Care, Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Sciences, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

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http://dx.doi.org/10.1007/s00134-020-06053-yDOI Listing
July 2020

Time to stop randomized and large pragmatic trials for intensive care medicine syndromes: the case of sepsis and acute respiratory distress syndrome.

J Thorac Dis 2020 Feb;12(Suppl 1):S101-S109

Department of Intensive Care Medicine, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands.

In this paper we discuss the limitations of large randomized controlled trials with mortality endpoints in patients with critical illness associated diagnoses such as sepsis. When patients with the same syndrome diagnosis do not share the pathways that lead to death (the attributable risk), any therapy can only lead to small effects in these populations. Using Monte Carlo simulations, we show how the syndrome-attributable risks of critical illness-associated diagnoses are likely overestimated using common statistical methods. This overestimation of syndrome-attributable risks leads to a corresponding overestimation of attainable treatment effects and an underestimation of required sample sizes. We demonstrate that larger and more 'pragmatic' randomized trials are not the solution because they decrease therapeutic and diagnostic precision, the therapeutic effect size and the probability of finding a beneficial effect. Finally, we argue that the most logical solution is a renewed focus on mechanistic research into the complexities of critical illness syndromes.
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http://dx.doi.org/10.21037/jtd.2019.10.36DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7024757PMC
February 2020

Ultrasound to Detect Central Venous Catheter Placement Associated Complications: A Multicenter Diagnostic Accuracy Study.

Anesthesiology 2020 04;132(4):781-794

From the Department of Intensive Care Medicine, Research VU University Medical Center (VUmc) Intensive Care, Amsterdam Cardiovascular Sciences, and Amsterdam Infection and Immunity Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands (J.M.S., M.E.H., E.H.T.L., T.S.S., H.R.W.T., A.R.J.G., L.M.A.H., P.R.T.) the Department of Intensive Care Medicine, Rijnstate Hospital, Arnhem, The Netherlands (M.J.B., F.H.B.) the Department of Intensive Care Medicine, Groene Hart Hospital, Gouda, The Netherlands (M.P., B.V.).

Background: Mechanical complications arising after central venous catheter placement are mostly malposition or pneumothorax. To date, to confirm correct position and detect pneumothorax, chest x-ray film has been the reference standard, while ultrasound might be an accurate alternative. The aim of this study was to evaluate diagnostic accuracy of ultrasound to detect central venous catheter malposition and pneumothorax.

Methods: This was a prospective, multicenter, diagnostic accuracy study conducted at the intensive care unit and postanesthesia care unit. Adult patients who underwent central venous catheterization of the internal jugular vein or subclavian vein were included. Index test consisted of venous, cardiac, and lung ultrasound. Standard reference test was chest x-ray film. Primary outcome was diagnostic accuracy of ultrasound to detect malposition and pneumothorax; for malposition, sensitivity, specificity, and other accuracy parameters were estimated. For pneumothorax, because chest x-ray film is an inaccurate reference standard to diagnose it, agreement and Cohen's κ-coefficient were determined. Secondary outcomes were accuracy of ultrasound to detect clinically relevant complications and feasibility of ultrasound.

Results: In total, 758 central venous catheterizations were included. Malposition occurred in 23 (3.3%) out of 688 cases included in the analysis. Ultrasound sensitivity was 0.70 (95% CI, 0.49 to 0.86) and specificity 0.99 (95% CI, 0.98 to 1.00). Pneumothorax occurred in 5 (0.7%) to 11 (1.5%) out of 756 cases according to chest x-ray film and ultrasound, respectively. In 748 out of 756 cases (98.9%), there was agreement between ultrasound and chest x-ray film with a Cohen's κ-coefficient of 0.50 (95% CI, 0.19 to 0.80).

Conclusions: This multicenter study shows that the complication rate of central venous catheterization is low and that ultrasound produces a moderate sensitivity and high specificity to detect malposition. There is moderate agreement with chest x-ray film for pneumothorax. In conclusion, ultrasound is an accurate diagnostic modality to detect malposition and pneumothorax.
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http://dx.doi.org/10.1097/ALN.0000000000003126DOI Listing
April 2020

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.

Intensive Care Med 2020 03 21;46(3):383-400. Epub 2020 Jan 21.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam, Amsterdam, The Netherlands.

Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.

Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.

Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.

Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.
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http://dx.doi.org/10.1007/s00134-019-05872-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067741PMC
March 2020
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